Improving the field efficacy of manuka oil using tank-mixes with surfactants and commercial organic herbicides
Bibliographic record
Abstract
Manuka oil was applied in combination with surfactants and other organic herbicides for a total of 10 different treatments. Three surfactants (Nu Film P, Agral 90, and yucca extract) and two essential oil based organic herbicides (clove–cinnamon oil and citrus oil) were tank-mixed with manuka oil. Herbicide treatments were analyzed against two checks (weedy and weed-free) in eight unique scenarios to determine what tank-mix options enhanced the preemergence (PRE) and postemergence (POST) efficacy of manuka oil. The eight unique scenarios included two locations (Simcoe and Ridgetown, ON), two crops (sweet corn and tomatoes), and two planting dates (early and late). Crop yield and visual weed efficacy data indicated that manuka oil had very weak PRE herbicidal properties in field scenarios. Manuka oil based treatments caused minimal crop injury on recently transplanted tomatoes and newly emerging sweet corn seedlings. Manuka oil was able to provide good weed control when applied POST either alone or in tank-mixes. The efficacy of manuka oil increased when manuka oil was used as a tank-mix partner compared with manuka oil applied alone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".